We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in terms of feedback focus and adaptivity. Previous evaluation efforts have examined feedback characteristics, its impact on learning, and its target, yet the focus of feedba...
Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coherence, and attribute-specific relevance. In this work, we introduce CASPER, a novel Chain-of-Thought Attribute-Specific Prompting for Evaluative Summarization framework that...
A. Bhardwaj, Arjit Singh Arora, Md. Shad Akhtar· 0 citations
Results show that selective anti-interference is an effective objective-level signal for robust context use and identifies locally sufficient tokens and sharply reduces prefix sensitivity on gate-selected suffix tokens.
Jin-Chang Zhu, Hao-Lan He, Yi-Cheng Ding et al.· 0 citations
It is shown that standard single-layer defenses each fail on their own and can even backfire, and called on the community to move beyond per-model alignment and toward composite safety mechanisms before multi-agent LLM systems are deployed at scale.
Zong-Hao Ying, Jia-Qi Yan, Hui-Ze Luo et al.· 0 citations
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This work examined AI-related discourse on r/teenagers from January 2023 to July 2026 using validated keyword-based retrieval and a human-in-the-loop, LLM-assisted thematic analysis to position adolescent AI use not simply as technology adoption, but as an emerging negotiation over AI's place and boundaries in everyday...
In statutory question answering, every claim must be traceable to evidence, not merely relevant, since unverifiable labour-rights answers carry serious legal consequences. Current systems fall short: single-pass RAG cannot detect insufficient evidence, while multi-agent legal-debate systems treat grounding as a prompti...
Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud et al.· 0 citations
A lightweight hierarchical attribution model is trained that predicts safety violations and attributes them to contributing user turns and token spans and preserves low false positive rates on benign conversations with high-risk vocabulary.
Srinivasan Subramanian, Kazi Aminul Islam, Md Abdullah Al Hafiz Khan· 0 citations
DeBERTa-based and T5-based models that rank HTML elements by their relevance to the task and a zero-shot ColBERT-based retriever that is able to retrieve the ground-truth element with a recall of 0.52 on Mind2Web and 0.47 on WebArena are developed.
Safety alignment in large language models is trained primarily in English, and recent work reports that the underlying harmfulness representation survives translation: English-trained probes separate harmful from harmless prompts almost as well in low-resource languages as in English. This has been taken as evidence th...
Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic populations require validation against real-world data, so researchers often address them using ad hoc comparisons with human surveys. Inspired by the intention-behavi...
F. Kutzner, C. Kacperski, Laura de Molière et al.· 1 citation
Jev has the lowest cost and median response time among the evaluated configurations, while hosted language models achieve higher baseline accuracy and Rankings by baseline accuracy differ from rankings by correctness across every condition and repeat, although small differences in the latter do not establish a general...
Fan Zhang, Yan-Kai Chen, Zhuo-Han Xie et al.· 13 citations· ⚡1
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
